Attempt in improving database migration: From Full Refresh to Incremental Loading

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1. What is DLT + Polars and Why These Libraries

Technology Stack Overview

This project implements a database migration pipeline using DLT (Data Load Tool) and Polars to handle PostgreSQL database replication from multiple production sources to a centralized analytics warehouse, orchestrated through Apache Airflow.

What is DLT (Data Load Tool)?

DLT is a Python library designed for building production-ready data pipelines with minimal boilerplate code.

Key DLT Features:

Incremental Loading: Built-in support for tracking changes and loading only new/modified data
Schema Management: Automatic schema inference, evolution, and type mapping
Pipeline State: Automatic tracking of load progress, failures, and recovery points
Multiple Destinations: Native support for PostgreSQL, Snowflake, BigQuery, and others
Write Dispositions: Flexible strategies (append, replace, merge) for different data loading patterns

Why DLT for Database Migration?

# DLT handles complex incremental logic automatically
@dlt.resource(
    write_disposition="merge",  # Intelligent merge strategy
    primary_key="id"           # Automatic deduplication
)
def table_resource(incremental: Incremental[datetime] = dlt.sources.incremental("updated_at")):
    # DLT automatically handles:
    # - Tracking last processed timestamp
    # - Querying only new/changed records
    # - Managing merge operations
    # - Error recovery and state persistence

What is Polars?

Polars is a fast DataFrame library written in Rust with Python bindings, designed for high-performance data processing.

Key Polars Advantages:

Speed: 10-30x faster than pandas for large datasets
Memory Efficiency: Lazy evaluation and columnar processing reduce memory usage
Database Integration: Native ConnectorX engine for fast database connectivity